A digital image signal processing apparatus digitally processes an analog or digital Bayer pattern signal provided from a complementary metal oxide semiconductor (CMOS) image sensor and/or a charge coupled device (CCD) to provide a displayable image signal. Accordingly, the digital image signal processing apparatus operates to restore an original signal as fully as possible from the Bayer pattern signal.
In order to facilitate storage and processing thereof, an image signal may be digitized. Accordingly, the original analog signal may be subjected to analog-to-digital conversion in an image sensor or a digital image signal processing apparatus. As is well known, digitizing is performed by sampling an analog signal. According to sampling theory, if a sampling frequency is not at least double the highest frequency component of the original signal, then aliasing, in which an incorrect frequency signal is output, may occur. To prevent aliasing, a low pass filter is typically used at a position where a signal is input to a digital image signal processing apparatus to attenuate high-frequency components of the input signal. However, the cut-off frequency of the low pass filter may be higher than the sampling frequency, potentially causing aliasing to occur due to high-frequency components of the input signal.
When aliasing occurs, a color component that does not exist in the original image, i.e., a pseudo-color, may appear at or near a high frequency edge of the digitized image. In particular, a pseudo-color may appear as noise in a high-frequency region of the digital image signal in which only a luminance component should be present. In this situation, the outline of an image may appear jagged in a stair-stepping appearance, which may reduce image quality.
FIG. 1 is a block diagram of a conventional digital image signal processing apparatus 100 having a line memory 10 including four lines and using a 5*5 edge detection mask to provide five line signals a−2 to a+2. FIG. 11 illustrates a Bayer signal corresponding to five lines provided through the line memory 10. Hereinafter, a conventional method of processing a digital image signal will be described with reference to FIGS. 1, 11 and 12.
The conventional digital image signal processing apparatus 100 includes a line memory 10 having a plurality of lines LM1 through LM4 to store a Bayer digital signal in line units. An interpolator 12 performs interpolation using Bayer pattern signals simultaneously provided in line units from the line memory 10 to generate interpolated RGB values for each pixel, and a color space converter 13 is configured to convert the interpolated RGB signal to a YCbCr format including a luminance component Y and chrominance components Cb/Cr. A pseudo-luminance generator 11 is configured to generate pseudo-luminance values to be used for edge detection using the Bayer pattern signals provided from the line memory 10. An edge detector 14 detects an edge in the image signal using the pseudo-luminance values, and an edge enhancer 15 enhances an edge of a luminance component signal Y obtained through color space conversion using an output of the edge detector 14. A color suppression coefficient calculator 16 performs a non-linear conversion using edge information provided from the edge detector 14 to calculate a color suppression coefficient, and a color suppressor 17 suppresses pseudo-color components included in the chrominance components Cb/Cr of the converted signal using the color suppression coefficient.
The pseudo-luminance generator 11 calculates a pseudo-luminance value using only green color values, which may primarily influence luminance from among the red, green, and blue (RGB) color values of a pixel.
Since an edge may be considered to be located at a portion of an image where the brightness of pixels changes rapidly, the luminance value Y, which corresponds to the brightness of a corresponding pixel, may be used for edge detection. Conventionally, after a signal is processed by the interpolator 12, i.e., after an RGB value is allocated to each pixel, a Y value of each pixel is extracted and used for edge detection. Since the Y value depends more on the green (G) than the red (R) or blue (B), the G value is typically directly used as the Y value. However, in order to use an interpolated G value as a pseudo-luminance value for edge detection, G values need to be provided for pixels in a window, e.g., a 5*5 window, needed for the edge detection, and, therefore, a line memory having almost the same size as the line memory 10 may be needed to store the interpolated G values for use in edge detection.
In order to reduce the burdens associated with an increase in the density of integration of a digital image processing device, a pseudo-luminance generator 11 as shown in FIG. 1 may be provided. That is, by using a pseudo-luminance generator 11 to generate pseudo-luminance values for use in edge detection, a separate line memory may not be required. For example, if the line memory 10 uses about 50,000 gates, the pseudo-luminance generator 11 may use less than about 1,000 gates, thus providing a significant advantage in terms of size.
FIG. 12 illustrates a 5×5 window, and FIG. 13 illustrates a Bayer pattern for all pixels in the 5×5 window illustrated in FIG. 12. Hereinafter, a conventional procedure of generating pseudo-luminance values YG−2, YG−1, YG, YG+1, and YG+2 will be described briefly with reference to FIGS. 12 and 13.
Usually, two kinds of methods are used to generate luminance. In both methods, a G value may be used as is when a pixel has G color.
In a first method, for example, for a pixel P11, a mean of four pixels P11, P12, P21, and P22, i.e., (P11+P12+P21+P22)/4 is used as a YG value.
In a second method, for example, for a pixel P33 having R color, a mean of two pixels, which are adjacent to the pixel P33 and have a least difference between their G values among a pair of pixels adjacent thereto in a horizontal direction and a pair of pixels adjacent thereto in a vertical direction, is used as a YG value. In other words, if a difference between pixels P32 and P34 is less than a difference between pixels P23 and P43, a mean of the two pixels P32 and P34, i.e., (P32+P34)/2 is used as the YG value.
When the YG value is provided to each line with this method, the edge detector 14 creates a 5×5 or 3*3 window comprised of YG values and detects an edge using the window. Edge detection is performed by generating an edge metric based appropriate G values and comparing the edge metric to a threshold to identify the presence or absence of an edge.
In conventional edge detection techniques, the pseudo-luminance values YG−2 through YG+2 for respective lines are calculated by taking a mean using a Bayer pattern signal, which is not converted into an RGB format through interpolation. Accordingly, the pseudo-luminance values YG−2 through YG+2 may be less accurate than Y values calculated using the color space converter 13 from RGB values that are obtained through interpolation,.
As a result, high-frequency components of an original image may be lost due to inaccurate Y values, and edge detection may increasingly fail. Pseudo-colors may appear at a portions of an image where edge detection fails.